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Bispectrum and recurrent neural networks: Improved classification of interictal and preictal states

Laura Gagliano, Elie Bou Assi, Dang K. Nguyen, Mohamad Sawan

Article (2019)

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Abstract

This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.

Uncontrolled Keywords

biomedical engineering, epilepsy

Subjects: 1900 Biomedical engineering > 1900 Biomedical engineering
1900 Biomedical engineering > 1901 Biomedical technology
Department: Department of Electrical Engineering
Institut de génie biomédical
Research Center: Other
Funders: NSERC / CRSNG, Epilepsy Canada, Institute for Data Valorization (IVADO)
PolyPublie URL: https://publications.polymtl.ca/4876/
Journal Title: Scientific Reports (vol. 9)
Publisher: Nature
DOI: 10.1038/s41598-019-52152-2
Official URL: https://doi.org/10.1038/s41598-019-52152-2
Date Deposited: 14 Jul 2021 11:19
Last Modified: 11 Nov 2022 14:17
Cite in APA 7: Gagliano, L., Bou Assi, E., Nguyen, D. K., & Sawan, M. (2019). Bispectrum and recurrent neural networks: Improved classification of interictal and preictal states. Scientific Reports, 9. https://doi.org/10.1038/s41598-019-52152-2

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